65 citations · 115 across the 23 of their papers we have counts for
11 papers · 1 filter
RECOUNT: Reference-guided Counting with Synthetic Visual Exemplars
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Text-guided zero-shot object counters excel at spatial localization but categorize poorly on novel or fine-grained classes: natural language is too coarse to fully specify visual i…
What Can We Learn from Inter-Annotator Variability in Skin Lesion Segmentation?
Kumar Abhishek, Jeremy Kawahara, Ghassan Hamarneh
Medical image segmentation exhibits intra- and inter-annotator variability due to ambiguous object boundaries, annotator preferences, expertise, and tools, among other factors. Les…
FiGO: Fine-Grained Object Counting without Annotations
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Class-agnostic counting (CAC) methods reduce annotation costs by letting users define what to count at test-time through text or visual exemplars. However, current open-vocabulary…
SMITE: Segment Me In TimE
Amirhossein Alimohammadi, Sauradip Nag, Saeid Asgari Taghanaki +3
Segmenting an object in a video presents significant challenges. Each pixel must be accurately labelled, and these labels must remain consistent across frames. The difficulty incre…
Lesion Elevation Prediction from Skin Images Improves Diagnosis
Kumar Abhishek, Ghassan Hamarneh
While deep learning-based computer-aided diagnosis for skin lesion image analysis is approaching dermatologists' performance levels, there are several works showing that incorporat…
Segmentation Style Discovery: Application to Skin Lesion Images
Kumar Abhishek, Jeremy Kawahara, Ghassan Hamarneh
Variability in medical image segmentation, arising from annotator preferences, expertise, and their choice of tools, has been well documented. While the majority of multi-annotator…
AFreeCA: Annotation-Free Counting for All
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Object counting methods typically rely on manually annotated datasets. The cost of creating such datasets has restricted the versatility of these networks to count objects from spe…
Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets
Kumar Abhishek, Aditi Jain, Ghassan Hamarneh
The remarkable progress of deep learning in dermatological tasks has brought us closer to achieving diagnostic accuracies comparable to those of human experts. However, while large…
SYRAC: Synthesize, Rank, and Count
Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Crowd counting is a critical task in computer vision, with several important applications. However, existing counting methods rely on labor-intensive density map annotations, neces…
SLiMe: Segment Like Me
Aliasghar Khani, Saeid Asgari Taghanaki, Aditya Sanghi +2
Significant strides have been made using large vision-language models, like Stable Diffusion (SD), for a variety of downstream tasks, including image editing, image correspondence,…
Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?
Weina Jin, Xiaoxiao Li, Ghassan Hamarneh
Being able to explain the prediction to clinical end-users is a necessity to leverage the power of artificial intelligence (AI) models for clinical decision support. For medical im…